Overview

Descriptive Statistics by City

Deployments Overview:
country city year citylabel
India Vellore 2014 Vellore 14
Mozambique Maputo 2015 Maputo 15
Ghana Accra 2016 Accra 16
Mozambique Maputo 2016 Maputo 16
Cambodia Siem Reap 2016 Siem Reap 16
USA Atlanta 2016 Atlanta 16
Bangladesh Dhaka 2017 Dhaka 17
Zambia Lusaka 2018 Lusaka 18
Ghana Accra 2018 Accra 18
Ghana Kumasi 2018 Kumasi 18
Uganda Kampala 2018 Kampala 18
Zambia Lusaka 2019 Lusaka 19
Senegal Dakar 2019 Dakar 19

In this database there are 13 SaniPath deployments.

Number of Neighborhoods

Neighborhoods Overview:
country city year neighborhood_id neighb_UID neighborhood note
India Vellore 2014 1 1101 Old Town
India Vellore 2014 2 1102 Chinna Allapuram
Mozambique Maputo 2015 1 501 Intervention
Mozambique Maputo 2015 2 502 Control
Ghana Accra 2016 1 301 Shiabu
Ghana Accra 2016 2 302 Chorkor
Ghana Accra 2016 3 303 Kokomlemle
Ghana Accra 2016 4 304 Ringway
Ghana Accra 2016 5 305 Adabraka
Mozambique Maputo 2016 1 801 Intervention
Mozambique Maputo 2016 2 802 Control
Cambodia Siem Reap 2016 1 101 Chong Kaosou Informal
Cambodia Siem Reap 2016 2 102 Kumruthemey (informal) Informal
Cambodia Siem Reap 2016 3 103 Kumruthemey (formal) Formal
Cambodia Siem Reap 2016 4 104 Steung Thumey Formal
Cambodia Siem Reap 2016 5 105 Veal/ Trapangses Formal
USA Atlanta 2016 1 1001 Peoplestown
Bangladesh Dhaka 2017 1 201 Kalshi North
Bangladesh Dhaka 2017 2 202 Badda North
Bangladesh Dhaka 2017 3 203 Gabtoli North
Bangladesh Dhaka 2017 4 204 Uttarkhan North
Bangladesh Dhaka 2017 5 205 Gulshan North
Bangladesh Dhaka 2017 6 206 Kamalapur South
Bangladesh Dhaka 2017 7 207 Shampur South
Bangladesh Dhaka 2017 8 208 Hazaribagh South
Bangladesh Dhaka 2017 9 209 Motijhil South
Bangladesh Dhaka 2017 10 210 Dhanmondi South
Zambia Lusaka 2018 1 401 Kanyama
Ghana Accra 2018 1 601 Mataheko
Ghana Accra 2018 2 602 Osu Alata
Ghana Kumasi 2018 1 701 FanteNewTown
Ghana Kumasi 2018 2 702 MoshieZongo
Ghana Kumasi 2018 3 703 Dakodwom
Ghana Kumasi 2018 4 704 Ahodwo
Uganda Kampala 2018 1 901 Makindye
Uganda Kampala 2018 2 902 Central
Uganda Kampala 2018 3 903 Kawempe
Uganda Kampala 2018 4 904 Rubaga
Uganda Kampala 2018 5 905 Nakawa
Zambia Lusaka 2019 1 1301 Chawama
Zambia Lusaka 2019 2 1302 Chazanga
Zambia Lusaka 2019 3 1303 George
Senegal Dakar 2019 1 1201 WakhinaneNimzatt
Senegal Dakar 2019 2 1202 MedinaGounass
Senegal Dakar 2019 3 1203 DTK
Senegal Dakar 2019 4 1204 RufisqueEst
Senegal Dakar 2019 5 1205 SicapLiberte

Overall, there are 13 deployments in 9 different countries, 10 cities, and 47 neighborhoods in this analysis.

Number of Samples per Pathway

Pathway Overview:
Vellore 14 Maputo 15 Accra 16 Maputo 16 Siem Reap 16 Atlanta 16 Dhaka 17 Lusaka 18 Accra 18 Kumasi 18 Kampala 18 Lusaka 19 Dakar 19
Open Drain Water 15 163 7 100 20 21 40 47 30 50
Raw Produce 20 70 23 33 10 100 20 20 39 50 30 50
Municipal Drinking Water 22 68 40 10 10 100 10 20 36 39 30 100
Ocean 30 10
Surface Water 100 3 12 10
Floodwater 10 9 10 50 7 100 30 9 36 50
Public Latrine 24 28 260 43 10 100 20 20 27 50 30
Soil 20 74 88 76 50 10 100 20 20 40 50 30 50
Bathing Water 20 25 25 10 100 9 21
Street Food 100 20 20 40 45 30 50
Other Drinking Water 150 100 30 39 60

Overall, 4053 samples were collected and analyized.

Survey Type and Number of Surveys per Deployment

Survey Type Overview:
Form Vellore 14 Maputo 15 Accra 16 Maputo 16 Siem Reap 16 Atlanta 16 Dhaka 17 Lusaka 18 Accra 18 Kumasi 18 Kampala 18 Lusaka 19 Dakar 19
Behavioral Survey
Household 200 125 821 136 410 23 823 100 200 400 548 300 500
Community 8 22 28 4 8 16 10 12 20
School 8 12 35 4 8 16 9 12 20
Environmental Testing
Sample 106 152 688 224 303 47 1000 170 149 282 382 250 300
Lab 106 152 688 224 303 47 1000 170 149 282 382 250 300

In total, there were 4586 Household Surveys, 128 Community Surveys and 124 School Surveys conducted. Additionally, 4053 environmental samples were collected and subsequently analyzed in the laboratory.

Survey Type and Number of People per Deployment

Form Participants:
Form Vellore 14 Maputo 15 Accra 16 Maputo 16 Siem Reap 16 Atlanta 16 Dhaka 17 Lusaka 18 Accra 18 Kumasi 18 Kampala 18 Lusaka 19 Dakar 19
Household 200 125 821 136 410 23 823 100 200 400 548 300 500
Community 117 293 501 79 127 240 112 219 300
School 151 315 597 73 120 320 114 240 300

Note: Household survey numbers include the number of surveys, whereas community and school surveys account for number of participants.

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Literature

Population Density

  • Cambodia, Siem Reap:
  • Bangladesh, Dhaka:
    Census 2011, Report: Female/ Male Population (Table C02), Population Density (Table C01)
  • Ghana, Accra:
    Census 2010, Report:
    Greater Accra Region: 1235.8 population density in 2010, 895.5 population density in 2000 (people per kmsq) (Table 4.4).
    90% of the population is considered urban population (Table 4.5).
  • Zambia, Lusaka:
    Census 2010, Report: Population density for Lusaka 63.5 (2000) and 100.1 (2010) people per kmsq (Table 2.7).
    Census 2010, Population.
  • Mozambique, Maputo:
    Note: added population information to meta_neighborhood.csv file. Sources: Ghana Statistical Services - Census 2010 Shapefile from Habib.

Enteric Disease Burden

Fecal Sludge Managment

Shit Flow Diagram

  • Cambodia, Siem Reap:
    no SFD available
  • Bangladesh, Dhaka:
    SFD Link. The SFD report from March 2016 indicates that less than 1% of fecal sludge in Dhaka is safely managed.
  • Ghana, Accra:
    no SFD available
  • Zambia, Lusaka:
    SFD Link. The SFD report from September 2018 shows that 17% of fecal sludge is safely managed.
  • Mozambique, Maputo:
    no SFD available
  • Ghana, Kumasi:
    SFD Link. The SFD report from November 2015 shows that 55% of fecal sludge is safely managed.

WASH Interventions in the past 10 Years

Local Stakeholders/ Decision Makers

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MF vs. IDEXX

How many samples were collected for each deployment and each pathway by laboratory analysis method: Membrane Filtration (MF) or IDEXX Quanti-Tray (IDEXX).

Lab Method used:
IDEXX MF
Vellore 14 0 106
Maputo 15 0 152
Accra 16 0 688
Maputo 16 0 224
Siem Reap 16 0 303
Atlanta 16 47 0
Dhaka 17 1000 0
Lusaka 18 170 0
Accra 18 0 149
Kumasi 18 0 282
Kampala 18 0 382
Lusaka 19 250 0
Dakar 19 0 300
Lab Method per Sample Type:
IDEXX MF
Open Drain Water 150 343
Raw Produce 160 305
Municipal Drinking Water 150 335
Ocean 0 40
Surface Water 110 15
Floodwater 137 174
Public Latrine 160 452
Soil 160 468
Bathing Water 100 110
Street Food 150 155
Other Drinking Water 190 189

Membrane Filtration: valid vs. invalid test results

How many samples tested with the Membrane Filtration method produced valid or invalid results?
Note: Invalid MF results include TDTC (Too Dirty To Count) classifications of laboratory results of samples. Valid MF results are all results between the range of 0 and 200 as well as TNTC (Too Numerous To Count).

Overview: Valid Membrane Filtration Lab Results
n valid invalid percent valid
6030 5846 184 96.95
Valid Membrane Filtration Lab Results by Deployment
citylabel n valid invalid percent valid
Vellore 14 212 212 0 100
Maputo 15 353 337 16 95.47
Accra 16 1539 1509 30 98.05
Maputo 16 564 503 61 89.18
Siem Reap 16 739 725 14 98.11
Accra 18 366 365 1 99.73
Kumasi 18 708 670 38 94.63
Kampala 18 947 924 23 97.57
Dakar 19 602 601 1 99.83
Valid Membrane Filtration Lab Results by Sample Type
sample type n valid invalid percent valid
Open Drain Water 877 872 5 99.43
Raw Produce 770 742 28 96.36
Municipal Drinking Water 671 671 0 100
Ocean 80 80 0 100
Surface Water 32 31 1 96.88
Floodwater 495 485 10 97.98
Public Latrine 910 890 20 97.8
Soil 1185 1073 112 90.55
Bathing Water 220 219 1 99.55
Street Food 410 408 2 99.51
Other Drinking Water 380 375 5 98.68
Valid Membrane Filtration Lab Results by Deployment and Sample Type
citylabel sample type n valid invalid percent valid
Vellore 14 Raw Produce 40 40 0 100
Vellore 14 Municipal Drinking Water 44 44 0 100
Vellore 14 Public Latrine 48 48 0 100
Vellore 14 Soil 40 40 0 100
Vellore 14 Bathing Water 40 40 0 100
Maputo 15 Open Drain Water 45 45 0 100
Maputo 15 Floodwater 30 27 3 90
Maputo 15 Public Latrine 56 56 0 100
Maputo 15 Soil 172 159 13 92.44
Maputo 15 Bathing Water 50 50 0 100
Accra 16 Open Drain Water 489 489 0 100
Accra 16 Raw Produce 140 135 5 96.43
Accra 16 Municipal Drinking Water 136 136 0 100
Accra 16 Ocean 60 60 0 100
Accra 16 Floodwater 18 18 0 100
Accra 16 Public Latrine 520 505 15 97.12
Accra 16 Soil 176 166 10 94.32
Maputo 16 Open Drain Water 21 21 0 100
Maputo 16 Raw Produce 69 67 2 97.1
Maputo 16 Municipal Drinking Water 80 80 0 100
Maputo 16 Floodwater 30 27 3 90
Maputo 16 Public Latrine 86 86 0 100
Maputo 16 Soil 228 172 56 75.44
Maputo 16 Bathing Water 50 50 0 100
Siem Reap 16 Raw Produce 99 94 5 94.95
Siem Reap 16 Municipal Drinking Water 20 20 0 100
Siem Reap 16 Floodwater 150 149 1 99.33
Siem Reap 16 Soil 150 147 3 98
Siem Reap 16 Bathing Water 20 20 0 100
Siem Reap 16 Other Drinking Water 300 295 5 98.33
Accra 18 Open Drain Water 42 42 0 100
Accra 18 Raw Produce 59 59 0 100
Accra 18 Municipal Drinking Water 40 40 0 100
Accra 18 Ocean 20 20 0 100
Accra 18 Floodwater 27 27 0 100
Accra 18 Public Latrine 40 40 0 100
Accra 18 Soil 60 59 1 98.33
Accra 18 Bathing Water 18 18 0 100
Accra 18 Street Food 60 60 0 100
Kumasi 18 Open Drain Water 80 79 1 98.75
Kumasi 18 Raw Produce 117 105 12 89.74
Kumasi 18 Municipal Drinking Water 72 72 0 100
Kumasi 18 Surface Water 6 6 0 100
Kumasi 18 Floodwater 97 94 3 96.91
Kumasi 18 Public Latrine 54 53 1 98.15
Kumasi 18 Soil 120 101 19 84.17
Kumasi 18 Bathing Water 42 41 1 97.62
Kumasi 18 Street Food 120 119 1 99.17
Kampala 18 Open Drain Water 100 97 3 97
Kampala 18 Raw Produce 146 142 4 97.26
Kampala 18 Municipal Drinking Water 78 78 0 100
Kampala 18 Surface Water 26 25 1 96.15
Kampala 18 Floodwater 143 143 0 100
Kampala 18 Public Latrine 106 102 4 96.23
Kampala 18 Soil 139 129 10 92.81
Kampala 18 Street Food 129 128 1 99.22
Kampala 18 Other Drinking Water 80 80 0 100
Dakar 19 Open Drain Water 100 99 1 99
Dakar 19 Raw Produce 100 100 0 100
Dakar 19 Municipal Drinking Water 201 201 0 100
Dakar 19 Soil 100 100 0 100
Dakar 19 Street Food 101 101 0 100

These results suggest sample types with a low percentage of validity of MF testing could improve the overall data quality by addressing potential sampling problems in training. Numbers in the “Percent Valid” column in RED indicate a rate below 80%.

IDEXX: valid vs. invalid test results

How many samples tested with IDEXX method produced a valid or invalid result?
Valid IDEXX Lab Results
n valid invalid percent valid
3668 3667 1 99.97

Dilution Error

When a dilution error occurs, meaning that a sample was not properly processed in the laboratory, the SaniPath Tool automatically discards the E. coli results of this sample (e.g. when the E. coli count for the second dilution is higher than the first dilution - including a variety of possible combinations of illogical results and potential errors).
This section explores how many samples were lost due to dilution error.

Dilution Error Overview:

Dilution Error by City
citylabel omitted n percentage
Vellore 14 9 106 8.49
Maputo 15 12 152 7.89
Accra 16 18 688 2.62
Maputo 16 10 224 4.46
Siem Reap 16 5 303 1.65
Atlanta 16 0 47 0.00
Dhaka 17 53 1000 5.30
Lusaka 18 18 170 10.59
Accra 18 0 149 0.00
Kumasi 18 3 282 1.06
Kampala 18 46 382 12.04
Lusaka 19 2 250 0.80
Dakar 19 15 300 5.00

Dilution Error By city, by neighborhood

Dilution Error by City and Neighborhood
citylabel neighb_UID omitted n percentage
Vellore 14 1101 2 53 3.77
Vellore 14 1102 7 53 13.21
Maputo 15 501 7 98 7.14
Maputo 15 502 5 54 9.26
Accra 16 301 3 93 3.23
Accra 16 302 4 253 1.58
Accra 16 303 7 124 5.65
Accra 16 304 2 85 2.35
Accra 16 305 2 133 1.50
Maputo 16 801 8 152 5.26
Maputo 16 802 2 72 2.78
Siem Reap 16 101 2 70 2.86
Siem Reap 16 102 2 60 3.33
Siem Reap 16 103 1 49 2.04
Dhaka 17 201 8 100 8.00
Dhaka 17 202 2 100 2.00
Dhaka 17 203 3 100 3.00
Dhaka 17 204 9 100 9.00
Dhaka 17 205 2 100 2.00
Dhaka 17 206 6 100 6.00
Dhaka 17 207 3 100 3.00
Dhaka 17 208 6 100 6.00
Dhaka 17 209 10 100 10.00
Dhaka 17 210 4 100 4.00
Lusaka 18 401 18 170 10.59
Kumasi 18 701 2 68 2.94
Kumasi 18 702 1 79 1.27
Kampala 18 901 14 67 20.90
Kampala 18 902 9 78 11.54
Kampala 18 903 2 78 2.56
Kampala 18 904 15 77 19.48
Kampala 18 905 6 82 7.32
Lusaka 19 1301 2 90 2.22
Dakar 19 1201 3 60 5.00
Dakar 19 1202 1 60 1.67
Dakar 19 1203 2 60 3.33
Dakar 19 1204 8 60 13.33
Dakar 19 1205 1 60 1.67

Dilution Error By city, by neighborhood, by sample

Dilution Error by City and Neighborhood and Sample Type
citylabel neighb_UID sample_type_name omitted n percentage
Vellore 14 1101 Municipal Drinking Water 2 11 18.18
Vellore 14 1102 Municipal Drinking Water 2 11 18.18
Vellore 14 1102 Public Latrine 1 12 8.33
Vellore 14 1102 Soil 1 10 10.00
Vellore 14 1102 Bathing Water 3 10 30.00
Maputo 15 501 Open Drain Water 1 9 11.11
Maputo 15 501 Soil 5 54 9.26
Maputo 15 501 Bathing Water 1 15 6.67
Maputo 15 502 Open Drain Water 1 6 16.67
Maputo 15 502 Floodwater 2 5 40.00
Maputo 15 502 Soil 2 20 10.00
Accra 16 301 Open Drain Water 1 16 6.25
Accra 16 301 Raw Produce 1 20 5.00
Accra 16 301 Soil 1 18 5.56
Accra 16 302 Raw Produce 2 20 10.00
Accra 16 302 Public Latrine 1 95 1.05
Accra 16 302 Soil 1 40 2.50
Accra 16 303 Raw Produce 5 9 55.56
Accra 16 303 Public Latrine 2 59 3.39
Accra 16 304 Raw Produce 1 10 10.00
Accra 16 304 Public Latrine 1 29 3.45
Accra 16 305 Raw Produce 2 11 18.18
Maputo 16 801 Municipal Drinking Water 1 30 3.33
Maputo 16 801 Public Latrine 5 30 16.67
Maputo 16 801 Soil 1 57 1.75
Maputo 16 801 Bathing Water 1 15 6.67
Maputo 16 802 Raw Produce 1 8 12.50
Maputo 16 802 Municipal Drinking Water 1 10 10.00
Siem Reap 16 101 Raw Produce 2 10 20.00
Siem Reap 16 102 Raw Produce 2 10 20.00
Siem Reap 16 103 Other Drinking Water 1 29 3.45
Dhaka 17 201 Open Drain Water 1 10 10.00
Dhaka 17 201 Raw Produce 1 10 10.00
Dhaka 17 201 Floodwater 2 10 20.00
Dhaka 17 201 Soil 2 10 20.00
Dhaka 17 201 Street Food 2 10 20.00
Dhaka 17 202 Raw Produce 2 10 20.00
Dhaka 17 203 Raw Produce 1 10 10.00
Dhaka 17 203 Soil 1 10 10.00
Dhaka 17 203 Street Food 1 10 10.00
Dhaka 17 204 Raw Produce 2 10 20.00
Dhaka 17 204 Surface Water 1 10 10.00
Dhaka 17 204 Floodwater 2 10 20.00
Dhaka 17 204 Soil 1 10 10.00
Dhaka 17 204 Street Food 3 10 30.00
Dhaka 17 205 Floodwater 1 10 10.00
Dhaka 17 205 Street Food 1 10 10.00
Dhaka 17 206 Raw Produce 2 10 20.00
Dhaka 17 206 Surface Water 1 10 10.00
Dhaka 17 206 Floodwater 1 10 10.00
Dhaka 17 206 Soil 1 10 10.00
Dhaka 17 206 Street Food 1 10 10.00
Dhaka 17 207 Raw Produce 2 10 20.00
Dhaka 17 207 Soil 1 10 10.00
Dhaka 17 208 Raw Produce 1 10 10.00
Dhaka 17 208 Floodwater 1 10 10.00
Dhaka 17 208 Soil 2 10 20.00
Dhaka 17 208 Street Food 2 10 20.00
Dhaka 17 209 Raw Produce 2 10 20.00
Dhaka 17 209 Surface Water 2 10 20.00
Dhaka 17 209 Soil 2 10 20.00
Dhaka 17 209 Street Food 4 10 40.00
Dhaka 17 210 Surface Water 1 10 10.00
Dhaka 17 210 Soil 1 10 10.00
Dhaka 17 210 Street Food 2 10 20.00
Lusaka 18 401 Open Drain Water 2 20 10.00
Lusaka 18 401 Raw Produce 1 20 5.00
Lusaka 18 401 Floodwater 10 30 33.33
Lusaka 18 401 Soil 4 20 20.00
Lusaka 18 401 Street Food 1 20 5.00
Kumasi 18 701 Raw Produce 1 9 11.11
Kumasi 18 701 Street Food 1 10 10.00
Kumasi 18 702 Public Latrine 1 9 11.11
Kampala 18 901 Raw Produce 1 10 10.00
Kampala 18 901 Surface Water 3 8 37.50
Kampala 18 901 Public Latrine 3 10 30.00
Kampala 18 901 Soil 2 10 20.00
Kampala 18 901 Street Food 5 10 50.00
Kampala 18 902 Raw Produce 4 10 40.00
Kampala 18 902 Soil 1 10 10.00
Kampala 18 902 Street Food 3 9 33.33
Kampala 18 902 Other Drinking Water 1 10 10.00
Kampala 18 903 Raw Produce 1 10 10.00
Kampala 18 903 Soil 1 10 10.00
Kampala 18 904 Raw Produce 3 10 30.00
Kampala 18 904 Floodwater 3 10 30.00
Kampala 18 904 Soil 4 10 40.00
Kampala 18 904 Street Food 4 10 40.00
Kampala 18 904 Other Drinking Water 1 10 10.00
Kampala 18 905 Floodwater 1 10 10.00
Kampala 18 905 Soil 2 10 20.00
Kampala 18 905 Other Drinking Water 3 10 30.00
Lusaka 19 1301 Soil 2 10 20.00
Dakar 19 1201 Soil 2 10 20.00
Dakar 19 1201 Street Food 1 10 10.00
Dakar 19 1202 Open Drain Water 1 10 10.00
Dakar 19 1203 Open Drain Water 1 10 10.00
Dakar 19 1203 Soil 1 10 10.00
Dakar 19 1204 Open Drain Water 7 10 70.00
Dakar 19 1204 Soil 1 10 10.00
Dakar 19 1205 Soil 1 10 10.00

Sample Size Minimum for Model:

To correctly perform the QMRA calculations, a minimum sample size of 10 samples has to be met. Which sample types per neighborhood and deployment have fulfilled the requirement? (omitted = sample discarded because of error)

Number of pathways that did not meet the requred 10 sample minimum, per city:
Requirements Not Met - Overview
citylabel neighb_UID not_met
Maputo 15 501 2
Maputo 15 502 2
Accra 16 303 2
Accra 16 304 1
Accra 16 305 1
Maputo 16 801 1
Maputo 16 802 3
Siem Reap 16 105 1
Atlanta 16 1001 1
Accra 18 601 2
Accra 18 602 2
Kumasi 18 701 3
Kumasi 18 702 1
Kumasi 18 703 3
Kumasi 18 704 2
Kampala 18 901 2
Kampala 18 902 2
Kampala 18 903 4
Kampala 18 904 1
Minimum Requirement Met?
citylabel neighb_UID sample_type_name omitted n requ_met net_n net_requ_met
Vellore 14 1101 Raw Produce 0 10 Yes 10 Yes
Vellore 14 1101 Municipal Drinking Water 2 11 Yes 9 No
Vellore 14 1101 Public Latrine 0 12 Yes 12 Yes
Vellore 14 1101 Soil 0 10 Yes 10 Yes
Vellore 14 1101 Bathing Water 0 10 Yes 10 Yes
Vellore 14 1102 Raw Produce 0 10 Yes 10 Yes
Vellore 14 1102 Municipal Drinking Water 2 11 Yes 9 No
Vellore 14 1102 Public Latrine 1 12 Yes 11 Yes
Vellore 14 1102 Soil 1 10 Yes 9 No
Vellore 14 1102 Bathing Water 3 10 Yes 7 No
Maputo 15 501 Open Drain Water 1 9 No 8 No
Maputo 15 501 Floodwater 0 5 No 5 No
Maputo 15 501 Public Latrine 0 15 Yes 15 Yes
Maputo 15 501 Soil 5 54 Yes 49 Yes
Maputo 15 501 Bathing Water 1 15 Yes 14 Yes
Maputo 15 502 Open Drain Water 1 6 No 5 No
Maputo 15 502 Floodwater 2 5 No 3 No
Maputo 15 502 Public Latrine 0 13 Yes 13 Yes
Maputo 15 502 Soil 2 20 Yes 18 Yes
Maputo 15 502 Bathing Water 0 10 Yes 10 Yes
Accra 16 301 Open Drain Water 1 16 Yes 15 Yes
Accra 16 301 Raw Produce 1 20 Yes 19 Yes
Accra 16 301 Municipal Drinking Water 0 10 Yes 10 Yes
Accra 16 301 Ocean 0 10 Yes 10 Yes
Accra 16 301 Public Latrine 0 19 Yes 19 Yes
Accra 16 301 Soil 1 18 Yes 17 Yes
Accra 16 302 Open Drain Water 0 48 Yes 48 Yes
Accra 16 302 Raw Produce 2 20 Yes 18 Yes
Accra 16 302 Municipal Drinking Water 0 30 Yes 30 Yes
Accra 16 302 Ocean 0 20 Yes 20 Yes
Accra 16 302 Public Latrine 1 95 Yes 94 Yes
Accra 16 302 Soil 1 40 Yes 39 Yes
Accra 16 303 Open Drain Water 0 37 Yes 37 Yes
Accra 16 303 Raw Produce 5 9 No 4 No
Accra 16 303 Municipal Drinking Water 0 9 No 9 No
Accra 16 303 Public Latrine 2 59 Yes 57 Yes
Accra 16 303 Soil 0 10 Yes 10 Yes
Accra 16 304 Open Drain Water 0 27 Yes 27 Yes
Accra 16 304 Raw Produce 1 10 Yes 9 No
Accra 16 304 Municipal Drinking Water 0 9 No 9 No
Accra 16 304 Public Latrine 1 29 Yes 28 Yes
Accra 16 304 Soil 0 10 Yes 10 Yes
Accra 16 305 Open Drain Water 0 35 Yes 35 Yes
Accra 16 305 Raw Produce 2 11 Yes 9 No
Accra 16 305 Municipal Drinking Water 0 10 Yes 10 Yes
Accra 16 305 Floodwater 0 9 No 9 No
Accra 16 305 Public Latrine 0 58 Yes 58 Yes
Accra 16 305 Soil 0 10 Yes 10 Yes
Maputo 16 801 Raw Produce 0 15 Yes 15 Yes
Maputo 16 801 Municipal Drinking Water 1 30 Yes 29 Yes
Maputo 16 801 Floodwater 0 5 No 5 No
Maputo 16 801 Public Latrine 5 30 Yes 25 Yes
Maputo 16 801 Soil 1 57 Yes 56 Yes
Maputo 16 801 Bathing Water 1 15 Yes 14 Yes
Maputo 16 802 Open Drain Water 0 7 No 7 No
Maputo 16 802 Raw Produce 1 8 No 7 No
Maputo 16 802 Municipal Drinking Water 1 10 Yes 9 No
Maputo 16 802 Floodwater 0 5 No 5 No
Maputo 16 802 Public Latrine 0 13 Yes 13 Yes
Maputo 16 802 Soil 0 19 Yes 19 Yes
Maputo 16 802 Bathing Water 0 10 Yes 10 Yes
Siem Reap 16 101 Raw Produce 2 10 Yes 8 No
Siem Reap 16 101 Floodwater 0 10 Yes 10 Yes
Siem Reap 16 101 Soil 0 10 Yes 10 Yes
Siem Reap 16 101 Bathing Water 0 10 Yes 10 Yes
Siem Reap 16 101 Other Drinking Water 0 30 Yes 30 Yes
Siem Reap 16 102 Raw Produce 2 10 Yes 8 No
Siem Reap 16 102 Floodwater 0 10 Yes 10 Yes
Siem Reap 16 102 Soil 0 10 Yes 10 Yes
Siem Reap 16 102 Other Drinking Water 0 30 Yes 30 Yes
Siem Reap 16 103 Floodwater 0 10 Yes 10 Yes
Siem Reap 16 103 Soil 0 10 Yes 10 Yes
Siem Reap 16 103 Other Drinking Water 1 29 Yes 28 Yes
Siem Reap 16 104 Raw Produce 0 10 Yes 10 Yes
Siem Reap 16 104 Municipal Drinking Water 0 10 Yes 10 Yes
Siem Reap 16 104 Floodwater 0 10 Yes 10 Yes
Siem Reap 16 104 Soil 0 10 Yes 10 Yes
Siem Reap 16 104 Other Drinking Water 0 31 Yes 31 Yes
Siem Reap 16 105 Raw Produce 0 3 No 3 No
Siem Reap 16 105 Floodwater 0 10 Yes 10 Yes
Siem Reap 16 105 Soil 0 10 Yes 10 Yes
Siem Reap 16 105 Other Drinking Water 0 30 Yes 30 Yes
Atlanta 16 1001 Raw Produce 0 10 Yes 10 Yes
Atlanta 16 1001 Municipal Drinking Water 0 10 Yes 10 Yes
Atlanta 16 1001 Floodwater 0 7 No 7 No
Atlanta 16 1001 Public Latrine 0 10 Yes 10 Yes
Atlanta 16 1001 Soil 0 10 Yes 10 Yes
Dhaka 17 201 Open Drain Water 1 10 Yes 9 No
Dhaka 17 201 Raw Produce 1 10 Yes 9 No
Dhaka 17 201 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 201 Surface Water 0 10 Yes 10 Yes
Dhaka 17 201 Floodwater 2 10 Yes 8 No
Dhaka 17 201 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 201 Soil 2 10 Yes 8 No
Dhaka 17 201 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 201 Street Food 2 10 Yes 8 No
Dhaka 17 201 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 202 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 202 Raw Produce 2 10 Yes 8 No
Dhaka 17 202 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 202 Surface Water 0 10 Yes 10 Yes
Dhaka 17 202 Floodwater 0 10 Yes 10 Yes
Dhaka 17 202 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 202 Soil 0 10 Yes 10 Yes
Dhaka 17 202 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 202 Street Food 0 10 Yes 10 Yes
Dhaka 17 202 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 203 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 203 Raw Produce 1 10 Yes 9 No
Dhaka 17 203 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 203 Surface Water 0 10 Yes 10 Yes
Dhaka 17 203 Floodwater 0 10 Yes 10 Yes
Dhaka 17 203 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 203 Soil 1 10 Yes 9 No
Dhaka 17 203 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 203 Street Food 1 10 Yes 9 No
Dhaka 17 203 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 204 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 204 Raw Produce 2 10 Yes 8 No
Dhaka 17 204 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 204 Surface Water 1 10 Yes 9 No
Dhaka 17 204 Floodwater 2 10 Yes 8 No
Dhaka 17 204 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 204 Soil 1 10 Yes 9 No
Dhaka 17 204 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 204 Street Food 3 10 Yes 7 No
Dhaka 17 204 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 205 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 205 Raw Produce 0 10 Yes 10 Yes
Dhaka 17 205 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 205 Surface Water 0 10 Yes 10 Yes
Dhaka 17 205 Floodwater 1 10 Yes 9 No
Dhaka 17 205 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 205 Soil 0 10 Yes 10 Yes
Dhaka 17 205 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 205 Street Food 1 10 Yes 9 No
Dhaka 17 205 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 206 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 206 Raw Produce 2 10 Yes 8 No
Dhaka 17 206 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 206 Surface Water 1 10 Yes 9 No
Dhaka 17 206 Floodwater 1 10 Yes 9 No
Dhaka 17 206 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 206 Soil 1 10 Yes 9 No
Dhaka 17 206 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 206 Street Food 1 10 Yes 9 No
Dhaka 17 206 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 207 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 207 Raw Produce 2 10 Yes 8 No
Dhaka 17 207 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 207 Surface Water 0 10 Yes 10 Yes
Dhaka 17 207 Floodwater 0 10 Yes 10 Yes
Dhaka 17 207 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 207 Soil 1 10 Yes 9 No
Dhaka 17 207 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 207 Street Food 0 10 Yes 10 Yes
Dhaka 17 207 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 208 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 208 Raw Produce 1 10 Yes 9 No
Dhaka 17 208 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 208 Surface Water 0 10 Yes 10 Yes
Dhaka 17 208 Floodwater 1 10 Yes 9 No
Dhaka 17 208 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 208 Soil 2 10 Yes 8 No
Dhaka 17 208 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 208 Street Food 2 10 Yes 8 No
Dhaka 17 208 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 209 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 209 Raw Produce 2 10 Yes 8 No
Dhaka 17 209 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 209 Surface Water 2 10 Yes 8 No
Dhaka 17 209 Floodwater 0 10 Yes 10 Yes
Dhaka 17 209 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 209 Soil 2 10 Yes 8 No
Dhaka 17 209 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 209 Street Food 4 10 Yes 6 No
Dhaka 17 209 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 210 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 210 Raw Produce 0 10 Yes 10 Yes
Dhaka 17 210 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 210 Surface Water 1 10 Yes 9 No
Dhaka 17 210 Floodwater 0 10 Yes 10 Yes
Dhaka 17 210 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 210 Soil 1 10 Yes 9 No
Dhaka 17 210 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 210 Street Food 2 10 Yes 8 No
Dhaka 17 210 Other Drinking Water 0 10 Yes 10 Yes
Lusaka 18 401 Open Drain Water 2 20 Yes 18 Yes
Lusaka 18 401 Raw Produce 1 20 Yes 19 Yes
Lusaka 18 401 Municipal Drinking Water 0 10 Yes 10 Yes
Lusaka 18 401 Floodwater 10 30 Yes 20 Yes
Lusaka 18 401 Public Latrine 0 20 Yes 20 Yes
Lusaka 18 401 Soil 4 20 Yes 16 Yes
Lusaka 18 401 Street Food 1 20 Yes 19 Yes
Lusaka 18 401 Other Drinking Water 0 30 Yes 30 Yes
Accra 18 601 Open Drain Water 0 10 Yes 10 Yes
Accra 18 601 Raw Produce 0 10 Yes 10 Yes
Accra 18 601 Municipal Drinking Water 0 10 Yes 10 Yes
Accra 18 601 Floodwater 0 4 No 4 No
Accra 18 601 Public Latrine 0 10 Yes 10 Yes
Accra 18 601 Soil 0 10 Yes 10 Yes
Accra 18 601 Bathing Water 0 4 No 4 No
Accra 18 601 Street Food 0 10 Yes 10 Yes
Accra 18 602 Open Drain Water 0 11 Yes 11 Yes
Accra 18 602 Raw Produce 0 10 Yes 10 Yes
Accra 18 602 Municipal Drinking Water 0 10 Yes 10 Yes
Accra 18 602 Ocean 0 10 Yes 10 Yes
Accra 18 602 Floodwater 0 5 No 5 No
Accra 18 602 Public Latrine 0 10 Yes 10 Yes
Accra 18 602 Soil 0 10 Yes 10 Yes
Accra 18 602 Bathing Water 0 5 No 5 No
Accra 18 602 Street Food 0 10 Yes 10 Yes
Kumasi 18 701 Open Drain Water 0 10 Yes 10 Yes
Kumasi 18 701 Raw Produce 1 9 No 8 No
Kumasi 18 701 Municipal Drinking Water 0 10 Yes 10 Yes
Kumasi 18 701 Floodwater 0 6 No 6 No
Kumasi 18 701 Public Latrine 0 10 Yes 10 Yes
Kumasi 18 701 Soil 0 10 Yes 10 Yes
Kumasi 18 701 Bathing Water 0 3 No 3 No
Kumasi 18 701 Street Food 1 10 Yes 9 No
Kumasi 18 702 Open Drain Water 0 10 Yes 10 Yes
Kumasi 18 702 Raw Produce 0 10 Yes 10 Yes
Kumasi 18 702 Municipal Drinking Water 0 10 Yes 10 Yes
Kumasi 18 702 Floodwater 0 10 Yes 10 Yes
Kumasi 18 702 Public Latrine 1 9 No 8 No
Kumasi 18 702 Soil 0 10 Yes 10 Yes
Kumasi 18 702 Bathing Water 0 10 Yes 10 Yes
Kumasi 18 702 Street Food 0 10 Yes 10 Yes
Kumasi 18 703 Open Drain Water 0 10 Yes 10 Yes
Kumasi 18 703 Raw Produce 0 10 Yes 10 Yes
Kumasi 18 703 Municipal Drinking Water 0 10 Yes 10 Yes
Kumasi 18 703 Surface Water 0 3 No 3 No
Kumasi 18 703 Floodwater 0 10 Yes 10 Yes
Kumasi 18 703 Public Latrine 0 4 No 4 No
Kumasi 18 703 Soil 0 10 Yes 10 Yes
Kumasi 18 703 Bathing Water 0 8 No 8 No
Kumasi 18 703 Street Food 0 10 Yes 10 Yes
Kumasi 18 704 Open Drain Water 0 10 Yes 10 Yes
Kumasi 18 704 Raw Produce 0 10 Yes 10 Yes
Kumasi 18 704 Municipal Drinking Water 0 6 No 6 No
Kumasi 18 704 Floodwater 0 10 Yes 10 Yes
Kumasi 18 704 Public Latrine 0 4 No 4 No
Kumasi 18 704 Soil 0 10 Yes 10 Yes
Kumasi 18 704 Street Food 0 10 Yes 10 Yes
Kampala 18 901 Open Drain Water 0 9 No 9 No
Kampala 18 901 Raw Produce 1 10 Yes 9 No
Kampala 18 901 Surface Water 3 8 No 5 No
Kampala 18 901 Floodwater 0 10 Yes 10 Yes
Kampala 18 901 Public Latrine 3 10 Yes 7 No
Kampala 18 901 Soil 2 10 Yes 8 No
Kampala 18 901 Street Food 5 10 Yes 5 No
Kampala 18 902 Open Drain Water 0 9 No 9 No
Kampala 18 902 Raw Produce 4 10 Yes 6 No
Kampala 18 902 Municipal Drinking Water 0 10 Yes 10 Yes
Kampala 18 902 Floodwater 0 10 Yes 10 Yes
Kampala 18 902 Public Latrine 0 10 Yes 10 Yes
Kampala 18 902 Soil 1 10 Yes 9 No
Kampala 18 902 Street Food 3 9 No 6 No
Kampala 18 902 Other Drinking Water 1 10 Yes 9 No
Kampala 18 903 Open Drain Water 0 10 Yes 10 Yes
Kampala 18 903 Raw Produce 1 10 Yes 9 No
Kampala 18 903 Municipal Drinking Water 0 9 No 9 No
Kampala 18 903 Surface Water 0 4 No 4 No
Kampala 18 903 Floodwater 0 10 Yes 10 Yes
Kampala 18 903 Public Latrine 0 10 Yes 10 Yes
Kampala 18 903 Soil 1 10 Yes 9 No
Kampala 18 903 Street Food 0 6 No 6 No
Kampala 18 903 Other Drinking Water 0 9 No 9 No
Kampala 18 904 Open Drain Water 0 7 No 7 No
Kampala 18 904 Raw Produce 3 10 Yes 7 No
Kampala 18 904 Municipal Drinking Water 0 10 Yes 10 Yes
Kampala 18 904 Floodwater 3 10 Yes 7 No
Kampala 18 904 Public Latrine 0 10 Yes 10 Yes
Kampala 18 904 Soil 4 10 Yes 6 No
Kampala 18 904 Street Food 4 10 Yes 6 No
Kampala 18 904 Other Drinking Water 1 10 Yes 9 No
Kampala 18 905 Open Drain Water 0 12 Yes 12 Yes
Kampala 18 905 Raw Produce 0 10 Yes 10 Yes
Kampala 18 905 Municipal Drinking Water 0 10 Yes 10 Yes
Kampala 18 905 Floodwater 1 10 Yes 9 No
Kampala 18 905 Public Latrine 0 10 Yes 10 Yes
Kampala 18 905 Soil 2 10 Yes 8 No
Kampala 18 905 Street Food 0 10 Yes 10 Yes
Kampala 18 905 Other Drinking Water 3 10 Yes 7 No
Lusaka 19 1301 Open Drain Water 0 10 Yes 10 Yes
Lusaka 19 1301 Raw Produce 0 10 Yes 10 Yes
Lusaka 19 1301 Municipal Drinking Water 0 10 Yes 10 Yes
Lusaka 19 1301 Surface Water 0 10 Yes 10 Yes
Lusaka 19 1301 Public Latrine 0 10 Yes 10 Yes
Lusaka 19 1301 Soil 2 10 Yes 8 No
Lusaka 19 1301 Street Food 0 10 Yes 10 Yes
Lusaka 19 1301 Other Drinking Water 0 20 Yes 20 Yes
Lusaka 19 1302 Open Drain Water 0 10 Yes 10 Yes
Lusaka 19 1302 Raw Produce 0 10 Yes 10 Yes
Lusaka 19 1302 Municipal Drinking Water 0 10 Yes 10 Yes
Lusaka 19 1302 Public Latrine 0 10 Yes 10 Yes
Lusaka 19 1302 Soil 0 10 Yes 10 Yes
Lusaka 19 1302 Street Food 0 10 Yes 10 Yes
Lusaka 19 1302 Other Drinking Water 0 20 Yes 20 Yes
Lusaka 19 1303 Open Drain Water 0 10 Yes 10 Yes
Lusaka 19 1303 Raw Produce 0 10 Yes 10 Yes
Lusaka 19 1303 Municipal Drinking Water 0 10 Yes 10 Yes
Lusaka 19 1303 Public Latrine 0 10 Yes 10 Yes
Lusaka 19 1303 Soil 0 10 Yes 10 Yes
Lusaka 19 1303 Street Food 0 10 Yes 10 Yes
Lusaka 19 1303 Other Drinking Water 0 20 Yes 20 Yes
Dakar 19 1201 Open Drain Water 0 10 Yes 10 Yes
Dakar 19 1201 Raw Produce 0 10 Yes 10 Yes
Dakar 19 1201 Municipal Drinking Water 0 20 Yes 20 Yes
Dakar 19 1201 Soil 2 10 Yes 8 No
Dakar 19 1201 Street Food 1 10 Yes 9 No
Dakar 19 1202 Open Drain Water 1 10 Yes 9 No
Dakar 19 1202 Raw Produce 0 10 Yes 10 Yes
Dakar 19 1202 Municipal Drinking Water 0 20 Yes 20 Yes
Dakar 19 1202 Soil 0 10 Yes 10 Yes
Dakar 19 1202 Street Food 0 10 Yes 10 Yes
Dakar 19 1203 Open Drain Water 1 10 Yes 9 No
Dakar 19 1203 Raw Produce 0 10 Yes 10 Yes
Dakar 19 1203 Municipal Drinking Water 0 20 Yes 20 Yes
Dakar 19 1203 Soil 1 10 Yes 9 No
Dakar 19 1203 Street Food 0 10 Yes 10 Yes
Dakar 19 1204 Open Drain Water 7 10 Yes 3 No
Dakar 19 1204 Raw Produce 0 10 Yes 10 Yes
Dakar 19 1204 Municipal Drinking Water 0 20 Yes 20 Yes
Dakar 19 1204 Soil 1 10 Yes 9 No
Dakar 19 1204 Street Food 0 10 Yes 10 Yes
Dakar 19 1205 Open Drain Water 0 10 Yes 10 Yes
Dakar 19 1205 Raw Produce 0 10 Yes 10 Yes
Dakar 19 1205 Municipal Drinking Water 0 20 Yes 20 Yes
Dakar 19 1205 Soil 1 10 Yes 9 No
Dakar 19 1205 Street Food 0 10 Yes 10 Yes

Sample Types

E. coli count per Sample Types
sample_type_name n min ecoli max ecoli mean ecoli standard deviation variance
Open Drain Water 493 -0.30 9.58 7.69 8.39 16.77
Raw Produce 465 1.40 7.00 5.29 6.04 12.08
Municipal Drinking Water 485 -0.30 4.76 2.70 3.55 7.10
Ocean 40 1.95 6.30 5.59 5.79 11.59
Surface Water 125 1.30 7.38 5.99 6.49 12.97
Floodwater 311 0.70 8.08 6.20 6.88 13.77
Public Latrine 612 -0.15 4.45 3.03 3.66 7.31
Soil 628 -1.00 5.60 3.97 4.68 9.35
Bathing Water 210 -0.30 4.38 2.85 3.48 6.97
Street Food 305 0.69 6.55 4.82 5.52 11.04
Other Drinking Water 379 -0.30 3.86 2.37 2.90 5.80

Samples - Membrane Filtration

E. coli count per Sample Types - Membrane Filtration
sample_type_name n min ecoli max ecoli mean ecoli standard deviation variance
Open Drain Water 343 -0.30 9.58 7.77 8.46 16.92
Raw Produce 305 1.40 7.00 5.44 6.13 12.25
Municipal Drinking Water 335 -0.30 3.30 1.47 2.28 4.55
Ocean 40 1.95 6.30 5.59 5.79 11.59
Surface Water 15 1.48 4.94 4.00 4.39 8.79
Floodwater 174 0.70 7.27 5.91 6.33 12.67
Public Latrine 452 -0.15 4.45 3.16 3.72 7.44
Soil 468 -1.00 5.60 4.05 4.73 9.46
Bathing Water 110 -0.30 3.88 2.44 2.93 5.87
Street Food 155 1.32 6.55 4.73 5.56 11.11
Other Drinking Water 189 -0.30 3.40 2.16 2.62 5.24

Samples - IDEXX

E. coli count per Sample Types - IDEXX
sample_type_name n min ecoli max ecoli mean ecoli standard deviation variance
Open Drain Water 150 2.00 8.71 7.40 7.78 15.56
Raw Produce 160 1.40 6.06 4.64 5.22 10.44
Municipal Drinking Water 150 -0.30 4.76 3.18 3.79 7.59
Surface Water 110 1.30 7.38 6.04 6.51 13.02
Floodwater 137 1.70 8.08 6.43 7.06 14.12
Public Latrine 160 -0.15 2.80 1.13 1.77 3.55
Soil 160 0.00 5.09 3.57 4.21 8.43
Bathing Water 100 -0.30 4.38 3.06 3.63 7.26
Street Food 150 0.69 6.44 4.91 5.47 10.94
Other Drinking Water 190 -0.30 3.86 2.51 3.01 6.03
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Produce

Contamination of Produce

Differences within Produce Samples

General comparison tables indicating how many samples were taken overall.

Types of Produce and Number of Samples
id produce n
1 cabbage 33
2 cucumber 73
3 egg plant 2
4 lettuce 34
5 long bean 2
6 long plant 2
7 salad 14
8 tomato 162
9 water mimosa 1
10 wing bean 2
11 apple 6
12 carrot 11
13 pepper 66
14 spring onion 3
15 guava 4
16 coriander 35
17 green chilly 10
18 okra 3
19 watermelon 1
Types of Produce, by Deployment
citylabel n produce
Vellore 14 8 tomato
Vellore 14 4 coriander
Vellore 14 5 green chilly
Vellore 14 3 okra
Accra 16 4 cabbage
Accra 16 1 cucumber
Accra 16 2 lettuce
Accra 16 22 tomato
Accra 16 1 apple
Accra 16 3 carrot
Accra 16 34 pepper
Accra 16 3 spring onion
Maputo 16 7 cucumber
Maputo 16 8 lettuce
Maputo 16 8 tomato
Siem Reap 16 4 cabbage
Siem Reap 16 8 cucumber
Siem Reap 16 2 egg plant
Siem Reap 16 5 lettuce
Siem Reap 16 2 long bean
Siem Reap 16 2 long plant
Siem Reap 16 4 salad
Siem Reap 16 3 tomato
Siem Reap 16 1 water mimosa
Siem Reap 16 2 wing bean
Atlanta 16 1 cucumber
Atlanta 16 2 lettuce
Atlanta 16 3 tomato
Atlanta 16 3 pepper
Dhaka 17 35 cucumber
Dhaka 17 34 tomato
Dhaka 17 31 coriander
Lusaka 18 11 cucumber
Lusaka 18 5 apple
Lusaka 18 4 guava
Accra 18 4 lettuce
Accra 18 9 tomato
Accra 18 7 pepper
Kumasi 18 13 lettuce
Kumasi 18 13 tomato
Kumasi 18 13 pepper
Kampala 18 25 cabbage
Kampala 18 25 tomato
Lusaka 19 1 cucumber
Lusaka 19 28 tomato
Lusaka 19 1 watermelon
Dakar 19 9 cucumber
Dakar 19 10 salad
Dakar 19 9 tomato
Dakar 19 8 carrot
Dakar 19 9 pepper
Dakar 19 5 green chilly
E. coli count per Types of Produce
produce n min ecoli max ecoli mean ecoli standard deviation variance
apple 6 1.40 2.41 1.81 1.98 3.96
cabbage 33 1.40 5.73 4.68 5.05 10.09
carrot 11 2.40 5.22 4.48 4.71 9.41
coriander 35 2.70 6.06 5.28 5.51 11.02
cucumber 73 1.40 7.00 5.69 6.32 12.64
egg plant 2 4.11 4.83 4.61 4.59 9.19
green chilly 10 2.40 6.00 5.34 5.61 11.21
guava 4 1.40 5.50 5.02 5.26 10.52
lettuce 34 1.40 7.00 5.65 6.24 12.48
long bean 2 2.00 2.48 2.30 2.15 4.30
long plant 2 3.22 3.48 3.37 2.98 5.96
okra 3 3.57 4.10 3.83 3.70 7.39
pepper 66 1.40 6.00 5.18 5.50 11.00
salad 14 2.40 5.82 5.20 5.29 10.58
spring onion 3 2.60 4.60 4.31 4.45 8.89
tomato 162 1.40 7.00 4.99 5.91 11.83
water mimosa 1 4.80 4.80 4.80
watermelon 1 1.40 1.40 1.40
wing bean 2 2.81 4.81 4.51 4.65 9.30
1 1.40 1.40 1.40

Graphs - Differences within Produce Samples

E. coli count by deployment and neighborhood and type of produce

E. coli count by deployment and neighborhood and type of produce

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Latrines

Latrines Types from Sample Collection

Types of Latrines in all deployments
n Type
45 Flush/pour flush to pit
310 Flush/pour flush to septic tank
66 Flush/pour flush to sewage system
70 Pit latrine with slab
75 VIP
4 Open pit latrine (without slab)
8 Other
## Warning: Column `col_UID`/`UID` joining factor and character vector,
## coercing into character vector
Types of Latrines by deployment
citylabel n Type
Maputo 15 4 Flush/pour flush to pit
Maputo 15 24 Pit latrine with slab
Accra 16 240 Flush/pour flush to septic tank
Accra 16 20 VIP
Maputo 16 19 Flush/pour flush to pit
Maputo 16 5 Pit latrine with slab
Maputo 16 17 VIP
Maputo 16 2 Other
Dhaka 17 6 Flush/pour flush to septic tank
Dhaka 17 64 Flush/pour flush to sewage system
Dhaka 17 10 Pit latrine with slab
Dhaka 17 12 VIP
Dhaka 17 4 Open pit latrine (without slab)
Dhaka 17 4 Other
Lusaka 18 2 Flush/pour flush to pit
Lusaka 18 3 Flush/pour flush to septic tank
Lusaka 18 1 Flush/pour flush to sewage system
Lusaka 18 11 Pit latrine with slab
Lusaka 18 3 VIP
Accra 18 19 Flush/pour flush to septic tank
Accra 18 1 Other
Kumasi 18 20 Flush/pour flush to septic tank
Kumasi 18 7 Pit latrine with slab
Kampala 18 4 Flush/pour flush to pit
Kampala 18 8 Flush/pour flush to septic tank
Kampala 18 1 Flush/pour flush to sewage system
Kampala 18 13 Pit latrine with slab
Kampala 18 23 VIP
Kampala 18 1 Other
Lusaka 19 16 Flush/pour flush to pit
Lusaka 19 14 Flush/pour flush to septic tank
Overall E. coli count per Types of Latrines
Type n min ecoli max ecoli mean ecoli standard deviation variance
Flush/pour flush to pit 45 -0.15 4.45 2.89 3.65 7.29
Flush/pour flush to septic tank 310 -0.15 4.45 3.21 3.76 7.52
Flush/pour flush to sewage system 66 -0.15 3.05 1.58 2.20 4.41
Pit latrine with slab 70 -0.15 3.45 2.30 2.80 5.61
VIP 75 -0.15 4.45 2.98 3.58 7.15
Open pit latrine (without slab) 4 -0.15 1.85 1.26 1.54 3.09
Other 8 -0.15 2.75 1.86 2.29 4.59
34 -0.15 3.45 2.89 3.06 6.12
E. coli count per Types of Latrines by Deployment
Type citylabel n min ecoli max ecoli mean ecoli standard deviation variance
Vellore 14 24 -0.15 3.45 3.04 3.09 6.18
Flush/pour flush to pit Maputo 15 4 -0.15 1.32 0.76 1.01 2.01
Pit latrine with slab Maputo 15 24 -0.15 3.45 2.56 2.97 5.95
Flush/pour flush to septic tank Accra 16 240 -0.15 4.45 3.31 3.81 7.62
VIP Accra 16 20 -0.15 4.45 3.52 3.83 7.66
Flush/pour flush to pit Maputo 16 19 -0.15 4.45 3.31 3.86 7.71
Pit latrine with slab Maputo 16 5 -0.15 0.62 0.23 0.16 0.32
VIP Maputo 16 17 -0.15 1.84 0.93 1.28 2.56
Other Maputo 16 2 0.45 2.75 2.45 2.60 5.19
Atlanta 16 10 -0.15 -0.15 -0.15 -Inf -Inf
Flush/pour flush to septic tank Dhaka 17 6 -0.15 1.88 1.13 1.49 2.98
Flush/pour flush to sewage system Dhaka 17 64 -0.15 2.80 1.33 1.94 3.88
Pit latrine with slab Dhaka 17 10 -0.15 2.10 1.45 1.69 3.37
VIP Dhaka 17 12 -0.15 1.19 0.30 0.63 1.25
Open pit latrine (without slab) Dhaka 17 4 -0.15 1.85 1.26 1.54 3.09
Other Dhaka 17 4 -0.15 -0.15 -0.15 -Inf -Inf
Flush/pour flush to pit Lusaka 18 2 -0.15 -0.15 -0.15 -Inf -Inf
Flush/pour flush to septic tank Lusaka 18 3 -0.15 -0.15 -0.15 -Inf -Inf
Flush/pour flush to sewage system Lusaka 18 1 -0.15 -0.15 -0.15
Pit latrine with slab Lusaka 18 11 -0.15 0.15 -0.08 -0.55 -1.10
VIP Lusaka 18 3 -0.15 -0.15 -0.15 -Inf -Inf
Flush/pour flush to septic tank Accra 18 19 -0.15 2.62 1.44 1.98 3.96
Other Accra 18 1 -0.15 -0.15 -0.15
Flush/pour flush to septic tank Kumasi 18 20 -0.15 4.05 2.90 3.43 6.85
Pit latrine with slab Kumasi 18 7 -0.15 3.10 2.27 2.68 5.35
Flush/pour flush to pit Kampala 18 4 -0.15 2.75 2.30 2.49 4.99
Flush/pour flush to septic tank Kampala 18 8 -0.15 2.92 2.08 2.47 4.93
Flush/pour flush to sewage system Kampala 18 1 3.05 3.05 3.05
Pit latrine with slab Kampala 18 13 -0.15 3.23 2.43 2.78 5.57
VIP Kampala 18 23 -0.15 3.53 2.23 2.85 5.71
Other Kampala 18 1 1.29 1.29 1.29
Flush/pour flush to pit Lusaka 19 16 -0.15 1.30 0.37 0.68 1.35
Flush/pour flush to septic tank Lusaka 19 14 -0.15 2.28 1.29 1.70 3.41
E. coli count of all latrines by Neighborhood
citylabel Neighborhood n min ecoli max ecoli mean ecoli standard deviation variance
Vellore 14 1101 12 0.15 3.45 3.07 3.10 6.20
Vellore 14 1102 12 -0.15 3.45 3.02 3.10 6.19
Maputo 15 501 15 -0.15 3.45 2.27 2.86 5.72
Maputo 15 502 13 -0.15 3.45 2.67 3.02 6.03
Accra 16 301 19 -0.15 4.45 3.72 3.94 7.88
Accra 16 302 95 -0.15 4.02 2.59 3.22 6.45
Accra 16 303 59 -0.15 4.45 3.56 3.94 7.88
Accra 16 304 29 -0.15 4.45 3.34 3.87 7.73
Accra 16 305 58 -0.15 4.45 3.37 3.84 7.69
Maputo 16 801 30 -0.15 3.23 2.04 2.55 5.10
Maputo 16 802 13 -0.15 4.45 3.34 3.89 7.78
Atlanta 16 1001 10 -0.15 -0.15 -0.15 -Inf -Inf
Dhaka 17 201 10 -0.15 1.88 0.92 1.38 2.76
Dhaka 17 202 10 -0.15 1.55 0.80 1.06 2.13
Dhaka 17 203 10 -0.15 1.85 0.89 1.34 2.69
Dhaka 17 204 10 -0.15 2.42 1.50 1.92 3.83
Dhaka 17 205 10 -0.15 -0.15 -0.15 -Inf -Inf
Dhaka 17 206 10 -0.15 2.80 1.89 2.29 4.58
Dhaka 17 207 10 -0.15 0.76 0.08 0.20 0.40
Dhaka 17 208 10 -0.15 2.10 1.48 1.68 3.36
Dhaka 17 209 10 -0.15 2.25 1.27 1.75 3.50
Dhaka 17 210 10 -0.15 0.76 0.11 0.20 0.40
Lusaka 18 401 20 -0.15 0.15 -0.11 -0.67 -1.33
Accra 18 601 10 -0.15 1.45 0.55 0.93 1.87
Accra 18 602 10 -0.15 2.62 1.69 2.12 4.23
Kumasi 18 701 10 0.75 3.10 2.16 2.59 5.19
Kumasi 18 702 9 -0.15 4.05 3.27 3.60 7.21
Kumasi 18 703 4 -0.15 1.32 0.97 0.95 1.90
Kumasi 18 704 4 -0.15 0.62 0.32 0.23 0.47
Kampala 18 901 10 -0.15 1.83 1.22 1.43 2.86
Kampala 18 902 10 -0.15 3.23 2.28 2.73 5.46
Kampala 18 903 10 0.85 3.15 2.61 2.73 5.46
Kampala 18 904 10 -0.15 -0.15 -0.15 -Inf -Inf
Kampala 18 905 10 -0.15 3.53 2.55 3.02 6.05
Lusaka 19 1301 10 -0.15 2.28 1.40 1.77 3.55
Lusaka 19 1302 10 -0.15 1.45 0.59 0.93 1.87
Lusaka 19 1303 10 -0.15 1.02 0.31 0.50 1.01

Graphs - Differences within Latrine Types

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Streetfood

Differences within Streetfood

Streetfood is standardized as one serving size of food available from street vendors.

## Warning: Column `col_UID`/`UID` joining factor and character vector,
## coercing into character vector
Streetfood E. coli count per deployment
citylabel n min ecoli max ecoli mean ecoli standard deviation variance
Dhaka 17 100 0.69 6.44 5.11 5.56 11.12
Lusaka 18 20 0.70 1.99 1.59 1.53 3.06
Accra 18 20 1.95 6.55 5.52 5.96 11.93
Kumasi 18 40 1.32 4.34 3.12 3.66 7.33
Kampala 18 45 1.54 5.56 4.37 4.86 9.72
Lusaka 19 30 0.98 1.99 1.55 1.26 2.51
Dakar 19 50 2.67 3.83 2.88 3.01 6.01
Streetfood E. coli count per neighborhood
neighb_UID citylabel n min ecoli max ecoli mean ecoli standard deviation variance
201 Dhaka 17 10 0.69 5.50 4.81 5.08 10.16
202 Dhaka 17 10 2.32 5.83 5.14 5.34 10.68
203 Dhaka 17 10 2.01 6.44 5.67 5.99 11.97
204 Dhaka 17 10 1.67 5.94 5.12 5.52 11.03
205 Dhaka 17 10 1.22 4.48 3.88 4.04 8.08
206 Dhaka 17 10 2.52 5.62 4.97 5.22 10.45
207 Dhaka 17 10 2.04 4.45 3.76 3.98 7.96
208 Dhaka 17 10 3.53 5.79 5.04 5.32 10.64
209 Dhaka 17 10 1.80 5.27 4.80 4.93 9.85
210 Dhaka 17 10 2.27 5.80 5.26 5.37 10.74
401 Lusaka 18 20 0.70 1.99 1.59 1.53 3.06
601 Accra 18 10 2.00 6.55 5.64 6.05 12.09
602 Accra 18 10 1.95 6.37 5.37 5.87 11.73
701 Kumasi 18 10 1.32 3.16 2.63 2.76 5.51
702 Kumasi 18 10 1.84 2.30 2.06 1.66 3.33
703 Kumasi 18 10 1.79 4.34 3.65 3.94 7.87
704 Kumasi 18 10 1.83 2.97 2.29 2.42 4.84
901 Kampala 18 10 1.54 5.56 4.92 5.20 10.40
902 Kampala 18 9 1.91 5.31 4.75 4.90 9.80
903 Kampala 18 6 1.90 2.26 2.09 1.67 3.34
904 Kampala 18 10 1.71 3.85 3.34 3.42 6.85
905 Kampala 18 10 1.70 3.49 2.61 2.98 5.95
1201 Dakar 19 10 2.67 3.00 2.76 2.24 4.47
1202 Dakar 19 10 2.67 2.83 2.74 1.95 3.89
1203 Dakar 19 10 2.67 2.83 2.74 1.94 3.89
1204 Dakar 19 10 2.67 3.61 2.98 3.04 6.08
1205 Dakar 19 10 2.67 3.83 3.07 3.30 6.59
1301 Lusaka 19 10 1.12 1.99 1.61 1.40 2.80
1302 Lusaka 19 10 1.45 1.84 1.59 1.04 2.09
1303 Lusaka 19 10 0.98 1.74 1.44 1.14 2.29

Graphs - Differences within Produce Samples

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Drinking Water

Differences within Drinking Water

Municipal Drinking Water E. coli count per deployment
citylabel n min ecoli max ecoli mean ecoli standard deviation variance
Vellore 14 22 -0.3 2.30 1.23 1.71 3.42
Accra 16 68 -0.3 3.30 1.96 2.55 5.09
Maputo 16 40 -0.3 2.77 1.46 1.99 3.98
Siem Reap 16 10 -0.3 -0.30 -0.30 -Inf -Inf
Atlanta 16 10 -0.3 -0.30 -0.30 -Inf -Inf
Dhaka 17 100 -0.3 4.76 3.36 3.88 7.75
Lusaka 18 10 -0.3 0.30 -0.19 -0.32 -0.65
Accra 18 20 -0.3 1.91 0.93 1.30 2.59
Kumasi 18 36 -0.3 3.22 1.71 2.44 4.88
Kampala 18 39 -0.3 1.11 -0.01 0.35 0.70
Lusaka 19 30 -0.3 2.27 1.17 1.65 3.29
Dakar 19 100 -0.3 0.00 -0.30 -1.30 -2.60
Municipal Drinking Water E. coli count per neighborhood
neighb_UID citylabel n min ecoli max ecoli mean ecoli standard deviation variance
104 Siem Reap 16 10 -0.30 -0.30 -0.30 -Inf -Inf
201 Dhaka 17 10 -0.30 1.38 0.70 0.90 1.79
202 Dhaka 17 10 -0.30 1.71 0.90 1.22 2.44
203 Dhaka 17 10 -0.30 1.35 0.64 0.86 1.71
204 Dhaka 17 10 -0.30 4.38 3.48 3.88 7.75
205 Dhaka 17 10 -0.30 2.18 1.19 1.68 3.36
206 Dhaka 17 10 -0.30 4.30 3.32 3.80 7.59
207 Dhaka 17 10 0.78 4.38 3.86 3.99 7.98
208 Dhaka 17 10 1.56 4.76 4.00 4.24 8.48
209 Dhaka 17 10 -0.30 2.62 2.18 2.18 4.36
210 Dhaka 17 10 -0.30 3.41 2.45 2.91 5.81
301 Accra 16 10 -0.30 1.38 0.95 1.01 2.03
302 Accra 16 30 -0.30 3.30 2.30 2.71 5.42
303 Accra 16 9 -0.30 0.00 -0.21 -0.66 -1.31
304 Accra 16 9 -0.30 0.48 -0.11 -0.08 -0.16
305 Accra 16 10 -0.30 -0.30 -0.30 -Inf -Inf
401 Lusaka 18 10 -0.30 0.30 -0.19 -0.32 -0.65
601 Accra 18 10 -0.30 1.91 1.00 1.40 2.81
602 Accra 18 10 -0.30 1.63 0.84 1.12 2.24
701 Kumasi 18 10 -0.30 1.49 1.08 1.02 2.03
702 Kumasi 18 10 -0.30 3.22 2.23 2.72 5.44
703 Kumasi 18 10 -0.30 0.30 -0.05 -0.34 -0.68
704 Kumasi 18 6 -0.30 1.40 0.66 1.00 2.00
801 Maputo 16 30 -0.30 2.77 1.53 2.05 4.09
802 Maputo 16 10 -0.30 1.72 1.10 1.26 2.52
902 Kampala 18 10 -0.30 -0.30 -0.30 -Inf -Inf
903 Kampala 18 9 -0.30 -0.30 -0.30 -Inf -Inf
904 Kampala 18 10 -0.30 -0.30 -0.30 -Inf -Inf
905 Kampala 18 10 -0.30 1.11 0.38 0.63 1.26
1001 Atlanta 16 10 -0.30 -0.30 -0.30 -Inf -Inf
1101 Vellore 14 11 -0.30 2.00 1.06 1.52 3.04
1102 Vellore 14 11 -0.30 2.30 1.36 1.82 3.65
1201 Dakar 19 20 -0.30 -0.30 -0.30 -Inf -Inf
1202 Dakar 19 20 -0.30 0.00 -0.28 -0.95 -1.90
1203 Dakar 19 20 -0.30 -0.30 -0.30 -Inf -Inf
1204 Dakar 19 20 -0.30 -0.30 -0.30 -Inf -Inf
1205 Dakar 19 20 -0.30 -0.30 -0.30 -Inf -Inf
1301 Lusaka 19 10 -0.30 -0.30 -0.30 -Inf -Inf
1302 Lusaka 19 10 -0.30 0.00 -0.26 -0.80 -1.60
1303 Lusaka 19 10 -0.30 2.27 1.64 1.85 3.69

Graphs - Differences within Drinking Water Samples

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#need to write separate function for this kind of Qs –>

Behavior - Comb

Adults - Combined Frequency

Yes/ No Questions

Children - Combined Frequency

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Exposure

Adults

The SaniPath exposure data is displayed here by pathway. The different colors represent the individual deployment, while a dot represents a particular neighborhood.

Children

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Map

Country Map

Neighborhood Map

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The end.